Deep Learning Dental CAD Automation
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Solution Overview
Problem
Current dental CAD systems rely heavily on manual labor and lack automation, making it difficult to achieve fully autonomous design without human interference, as each dental restoration is unique and requires complex decision-making that is hard to define rigorously.
Innovation Solution
The use of deep learning techniques to train neural networks on large datasets of dental scans and prosthesis data, allowing for the generation of 3D dental prosthesis models based on patient scan data, which can include customization features matching patient profiles such as gender, age, and lifestyle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labor is used in dental CAD design, then flexibility and adaptability to unique cases is maintained, but productivity and efficiency are reduced
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated deep learning-based system. The neural network automatically generates 3D dental prosthesis models from patient scan data, eliminating the need for manual modeling while maintaining high design efficiency and customization capability.
Solution Approach 2:
The system enables self-service automation where the deep learning model independently performs the design task without human intervention. The neural network processes patient data, generates designs, and creates final models autonomously, achieving full automation while maintaining adaptability to unique dental cases through learned patterns from training data.
2Reliability
If comprehensive rules are formulated to capture dental professional expertise, then design accuracy and reliability are improved, but device complexity and development time increase significantly
Solution Approach 1:
The patent substitutes the complex mechanical task of formulating comprehensive design rules with a deep learning neural network. The system learns dental design principles from training data consisting of example cases and expert annotations, automatically capturing the necessary rules without requiring explicit programming of complex decision-making logic.
Solution Approach 2:
The system copies and learns from existing successful dental designs in the training data. By analyzing numerous example cases and expert annotations, the neural network internalizes design patterns and principles, enabling it to generate reliable designs for new cases without requiring explicit formulation of all possible rules.
3Productivity
If deep learning is used to automate dental CAD design, then productivity and automation level are improved, but requirements for large datasets and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning neural network on a comprehensive dataset of dental cases before actual use. This pre-training phase consumes computational resources and data, but once completed, the system can efficiently generate designs for new patients without requiring continuous large-scale data processing, thereby achieving high productivity with manageable resource requirements.
Data Source
AI summary
Computer-implemented methods for generating a 3D dental prosthesis model are disclosed herein. The methods comprise training a deep neural network to generate a first 3D dental prosthesis model using a training data set; receiving a patient scan data representing at least a portion of a patient's dentition; and generating, using the trained deep neural network, the first 3D dental prosthesis model based on the received patient scan data.